An efficient edge detection using Raster CNN simulator

Authors:
V. Murugesh;Kyung-Tae Kim
Affiliations:
Department of Microsoft Information Technology, Keimyung Adams College, Keimyung University, Daegu, Republic of Korea;Department of Information and Communications Engineering, Hannam University, Daejeon, Republic of Korea
Venue:
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
Year:
2011

Citing 5
Cited 0

A Computational Approach to Edge Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical theory of edge detection

Computer Vision, Graphics, and Image Processing
Edge detection in correlated noise using Latin Square masks

Pattern Recognition
Line detection in noisy and structured backgrounds using Græco-Latin squares

CVGIP: Graphical Models and Image Processing
Image processing applications via time-multiplexing cellular neural network simulator with numerical integration algorithms

International Journal of Computer Mathematics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a cellular neural network based edge detection using Raster CNN Simulator. The software is designed to handle with both gray level and color images. The experimental result of Raster CNN Simulator is compared with traditional edge detection operators Canny and Sobel. Simulation results show that the proposed simulator is accurately detecting the complete image edge and also save the computation time.